library("FRESA.CAD")
library(readxl)
library(igraph)
library(umap)
library(tsne)
library(entropy)
library(mlbench)
library(psych)
library(whitening)
library("vioplot")
library("rpart")
op <- par(no.readonly = TRUE)
pander::panderOptions('digits', 3)
pander::panderOptions('table.split.table', 400)
pander::panderOptions('keep.trailing.zeros',TRUE)
mlBench library
Newman, D.J. & Hettich, S. & Blake, C.L. & Merz, C.J. (1998). UCI Repository of machine learning databases [http://www.ics.uci.edu/~mlearn/MLRepository.html]. Irvine, CA: University of California, Department of Information and Computer Science.
data(PimaIndiansDiabetes)
pander::pander(table(PimaIndiansDiabetes$diabetes))
| neg | pos |
|---|---|
| 500 | 268 |
PimaIndiansDiabetes$diabetes <- 1*(PimaIndiansDiabetes$diabetes=="pos")
studyName <- "Diabetes"
dataframe <- PimaIndiansDiabetes
outcome <- "diabetes"
thro <- 0.25
TopVariables <- 3
cexheat = 0.45
Some libraries
library(psych)
library(whitening)
library("vioplot")
library("rpart")
pander::pander(c(rows=nrow(dataframe),col=ncol(dataframe)-1))
| rows | col |
|---|---|
| 768 | 8 |
pander::pander(table(dataframe[,outcome]))
| 0 | 1 |
|---|---|
| 500 | 268 |
varlist <- colnames(dataframe)
varlist <- varlist[varlist != outcome]
largeSet <- length(varlist) > 1500
Scaling and removing near zero variance columns and highly co-linear(r>0.99999) columns
### Some global cleaning
sdiszero <- apply(dataframe,2,sd) > 1.0e-16
dataframe <- dataframe[,sdiszero]
varlist <- colnames(dataframe)[colnames(dataframe) != outcome]
tokeep <- c(as.character(correlated_Remove(dataframe,varlist,thr=0.99999)),outcome)
dataframe <- dataframe[,tokeep]
varlist <- colnames(dataframe)
varlist <- varlist[varlist != outcome]
iscontinous <- sapply(apply(dataframe,2,unique),length) > 5 ## Only variables with enough samples
dataframeScaled <- FRESAScale(dataframe,method="OrderLogit")$scaledData
numsub <- nrow(dataframe)
if (numsub > 1000) numsub <- 1000
if (!largeSet)
{
hm <- heatMaps(data=dataframeScaled[1:numsub,],
Outcome=outcome,
Scale=TRUE,
hCluster = "row",
xlab="Feature",
ylab="Sample",
srtCol=45,
srtRow=45,
cexCol=cexheat,
cexRow=cexheat
)
par(op)
}
The heat map of the data
if (!largeSet)
{
par(cex=0.6,cex.main=0.85,cex.axis=0.7)
#cormat <- Rfast::cora(as.matrix(dataframe[,varlist]),large=TRUE)
cormat <- cor(dataframe[,varlist],method="pearson")
cormat[is.na(cormat)] <- 0
gplots::heatmap.2(abs(cormat),
trace = "none",
# scale = "row",
mar = c(5,5),
col=rev(heat.colors(5)),
main = "Original Correlation",
cexRow = cexheat,
cexCol = cexheat,
srtCol=45,
srtRow=45,
key.title=NA,
key.xlab="|Pearson Correlation|",
xlab="Feature", ylab="Feature")
diag(cormat) <- 0
print(max(abs(cormat)))
}
[1]
0.5443412
DEdataframe <- IDeA(dataframe,verbose=TRUE,thr=thro)
#>
#> Included: 8 , Uni p: 0.01082532 , Uncorrelated Base: 4 , Outcome-Driven Size: 0 , Base Size: 4
#>
#>
1 <R=0.544,r=0.397,N= 4>, Top: 2( 1 )[ 1 : 2 Fa= 2 : 0.397 ]( 2 , 2 , 0 ),<|>Tot Used: 4 , Added: 2 , Zero Std: 0 , Max Cor: 0.393
#>
2 <R=0.393,r=0.321,N= 4>, Top: 2( 1 )[ 1 : 2 Fa= 3 : 0.321 ]( 2 , 2 , 2 ),<|>Tot Used: 6 , Added: 2 , Zero Std: 0 , Max Cor: 0.230
#>
3 <R=0.230,r=0.250,N= 0>
#>
[ 3 ], 0.2301263 Decor Dimension: 6 Nused: 6 . Cor to Base: 3 , ABase: 2 , Outcome Base: 0
#>
varlistc <- colnames(DEdataframe)[colnames(DEdataframe) != outcome]
pander::pander(sum(apply(dataframe[,varlist],2,var)))
15144
pander::pander(sum(apply(DEdataframe[,varlistc],2,var)))
11314
pander::pander(entropy(discretize(unlist(dataframe[,varlist]), 256)))
3.24
pander::pander(entropy(discretize(unlist(DEdataframe[,varlistc]), 256)))
3.62
if (!largeSet)
{
par(cex=0.6,cex.main=0.85,cex.axis=0.7)
UPSTM <- attr(DEdataframe,"UPSTM")
gplots::heatmap.2(1.0*(abs(UPSTM)>0),
trace = "none",
mar = c(5,5),
col=rev(heat.colors(5)),
main = "Decorrelation matrix",
cexRow = cexheat,
cexCol = cexheat,
srtCol=45,
srtRow=45,
key.title=NA,
key.xlab="|Beta|>0",
xlab="Output Feature", ylab="Input Feature")
par(op)
}
if (!largeSet)
{
cormat <- cor(DEdataframe[,varlistc],method="pearson")
cormat[is.na(cormat)] <- 0
gplots::heatmap.2(abs(cormat),
trace = "none",
mar = c(5,5),
col=rev(heat.colors(5)),
main = "Correlation after IDeA",
cexRow = cexheat,
cexCol = cexheat,
srtCol=45,
srtRow=45,
key.title=NA,
key.xlab="|Pearson Correlation|",
xlab="Feature", ylab="Feature")
par(op)
diag(cormat) <- 0
print(max(abs(cormat)))
}
[1]
0.2301263
if (nrow(dataframe) < 1000)
{
classes <- unique(dataframe[1:numsub,outcome])
raincolors <- rainbow(length(classes))
names(raincolors) <- classes
datasetframe.umap = umap(scale(dataframe[1:numsub,varlist]),n_components=2)
plot(datasetframe.umap$layout,xlab="U1",ylab="U2",main="UMAP: Original",t='n')
text(datasetframe.umap$layout,labels=dataframe[1:numsub,outcome],col=raincolors[dataframe[1:numsub,outcome]+1])
}
if (nrow(dataframe) < 1000)
{
datasetframe.umap = umap(scale(DEdataframe[1:numsub,varlistc]),n_components=2)
plot(datasetframe.umap$layout,xlab="U1",ylab="U2",main="UMAP: After IDeA",t='n')
text(datasetframe.umap$layout,labels=DEdataframe[1:numsub,outcome],col=raincolors[DEdataframe[1:numsub,outcome]+1])
}
univarRAW <- uniRankVar(varlist,
paste(outcome,"~1"),
outcome,
dataframe,
rankingTest="AUC")
univarDe <- uniRankVar(varlistc,
paste(outcome,"~1"),
outcome,
DEdataframe,
rankingTest="AUC",
)
univariate_columns <- c("caseMean","caseStd","controlMean","controlStd","controlKSP","ROCAUC")
##topfive
topvar <- c(1:length(varlist)) <= TopVariables
pander::pander(univarRAW$orderframe[topvar,univariate_columns])
| caseMean | caseStd | controlMean | controlStd | controlKSP | ROCAUC | |
|---|---|---|---|---|---|---|
| glucose | 141.3 | 31.94 | 110.0 | 26.14 | 0.0472 | 0.788 |
| mass | 35.1 | 7.26 | 30.3 | 7.69 | 0.0408 | 0.688 |
| age | 37.1 | 10.97 | 31.2 | 11.67 | 0.0000 | 0.687 |
topLAvar <- univarDe$orderframe$Name[str_detect(univarDe$orderframe$Name,"La_")]
topLAvar <- unique(c(univarDe$orderframe$Name[topvar],topLAvar[1:as.integer(TopVariables/2)]))
finalTable <- univarDe$orderframe[topLAvar,univariate_columns]
theLaVar <- rownames(finalTable)[str_detect(rownames(finalTable),"La_")]
pander::pander(univarDe$orderframe[topLAvar,univariate_columns])
| caseMean | caseStd | controlMean | controlStd | controlKSP | ROCAUC | |
|---|---|---|---|---|---|---|
| glucose | 141.26 | 31.94 | 110.0 | 26.14 | 4.72e-02 | 0.788 |
| La_mass | 30.84 | 7.00 | 26.5 | 6.93 | 2.12e-02 | 0.686 |
| pregnant | 4.87 | 3.74 | 3.3 | 3.02 | 1.11e-16 | 0.620 |
dc <- getLatentCoefficients(DEdataframe)
fscores <- attr(DEdataframe,"fscore")
theSigDc <- dc[theLaVar]
names(theSigDc) <- NULL
theSigDc <- unique(names(unlist(theSigDc)))
theFormulas <- dc[rownames(finalTable)]
deFromula <- character(length(theFormulas))
names(deFromula) <- rownames(finalTable)
pander::pander(c(mean=mean(sapply(dc,length)),total=length(dc),fraction=length(dc)/(ncol(dataframe)-1)))
| mean | total | fraction |
|---|---|---|
| 2.33 | 3 | 0.375 |
allSigvars <- names(dc)
dx <- names(deFromula)[1]
for (dx in names(deFromula))
{
coef <- theFormulas[[dx]]
cname <- names(theFormulas[[dx]])
names(cname) <- cname
for (cf in names(coef))
{
if (cf != dx)
{
if (coef[cf]>0)
{
deFromula[dx] <- paste(deFromula[dx],
sprintf("+ %5.3f*%s",coef[cf],cname[cf]))
}
else
{
deFromula[dx] <- paste(deFromula[dx],
sprintf("%5.3f*%s",coef[cf],cname[cf]))
}
}
}
}
finalTable <- rbind(finalTable,univarRAW$orderframe[theSigDc[!(theSigDc %in% rownames(finalTable))],univariate_columns])
orgnamez <- rownames(finalTable)
orgnamez <- str_remove_all(orgnamez,"La_")
finalTable$RAWAUC <- univarRAW$orderframe[orgnamez,"ROCAUC"]
finalTable$DecorFormula <- deFromula[rownames(finalTable)]
finalTable$fscores <- fscores[rownames(finalTable)]
Final_Columns <- c("DecorFormula","caseMean","caseStd","controlMean","controlStd","controlKSP","ROCAUC","RAWAUC","fscores")
finalTable <- finalTable[order(-finalTable$ROCAUC),]
pander::pander(finalTable[,Final_Columns])
| DecorFormula | caseMean | caseStd | controlMean | controlStd | controlKSP | ROCAUC | RAWAUC | fscores | |
|---|---|---|---|---|---|---|---|---|---|
| glucose | 141.26 | 31.94 | 110.0 | 26.14 | 4.72e-02 | 0.788 | 0.788 | 1 | |
| mass | NA | 35.14 | 7.26 | 30.3 | 7.69 | 4.08e-02 | 0.688 | 0.688 | NA |
| La_mass | -0.194triceps + 1.000mass | 30.84 | 7.00 | 26.5 | 6.93 | 2.12e-02 | 0.686 | 0.688 | -1 |
| pregnant | 4.87 | 3.74 | 3.3 | 3.02 | 1.11e-16 | 0.620 | 0.620 | 1 | |
| triceps | NA | 22.16 | 17.68 | 19.7 | 14.89 | 3.11e-15 | 0.554 | 0.554 | 2 |
featuresnames <- colnames(dataframe)[colnames(dataframe) != outcome]
pc <- prcomp(dataframe[,iscontinous],center = TRUE,tol=0.002) #principal components
predPCA <- predict(pc,dataframe[,iscontinous])
PCAdataframe <- as.data.frame(cbind(predPCA,dataframe[,!iscontinous]))
colnames(PCAdataframe) <- c(colnames(predPCA),colnames(dataframe)[!iscontinous])
#plot(PCAdataframe[,colnames(PCAdataframe)!=outcome],col=dataframe[,outcome],cex=0.65,cex.lab=0.5,cex.axis=0.75,cex.sub=0.5,cex.main=0.75)
#pander::pander(pc$rotation)
PCACor <- cor(PCAdataframe[,colnames(PCAdataframe) != outcome])
gplots::heatmap.2(abs(PCACor),
trace = "none",
# scale = "row",
mar = c(5,5),
col=rev(heat.colors(5)),
main = "PCA Correlation",
cexRow = 0.5,
cexCol = 0.5,
srtCol=45,
srtRow= -45,
key.title=NA,
key.xlab="Pearson Correlation",
xlab="Feature", ylab="Feature")
EFAdataframe <- dataframeScaled
if (length(iscontinous) < 2000)
{
topred <- min(length(iscontinous),nrow(dataframeScaled),ncol(predPCA)/2)
if (topred < 2) topred <- 2
uls <- fa(dataframeScaled[,iscontinous],nfactors=topred,rotate="varimax",warnings=FALSE) # EFA analysis
predEFA <- predict(uls,dataframeScaled[,iscontinous])
EFAdataframe <- as.data.frame(cbind(predEFA,dataframeScaled[,!iscontinous]))
colnames(EFAdataframe) <- c(colnames(predEFA),colnames(dataframeScaled)[!iscontinous])
EFACor <- cor(EFAdataframe[,colnames(EFAdataframe) != outcome])
gplots::heatmap.2(abs(EFACor),
trace = "none",
# scale = "row",
mar = c(5,5),
col=rev(heat.colors(5)),
main = "EFA Correlation",
cexRow = 0.5,
cexCol = 0.5,
srtCol=45,
srtRow= -45,
key.title=NA,
key.xlab="Pearson Correlation",
xlab="Feature", ylab="Feature")
}
par(op)
par(xpd = TRUE)
dataframe[,outcome] <- factor(dataframe[,outcome])
rawmodel <- rpart(paste(outcome,"~."),dataframe,control=rpart.control(maxdepth=3))
pr <- predict(rawmodel,dataframe,type = "class")
ptab <- list(er="Error",detail=matrix(nrow=6,ncol=1))
if (length(unique(pr))>1)
{
plot(rawmodel,main="Raw",branch=0.5,uniform = TRUE,compress = TRUE,margin=0.1)
text(rawmodel, use.n = TRUE,cex=0.75)
ptab <- epiR::epi.tests(table(pr==0,dataframe[,outcome]==0))
}
pander::pander(table(dataframe[,outcome],pr))
| 0 | 1 | |
|---|---|---|
| 0 | 443 | 57 |
| 1 | 118 | 150 |
pander::pander(ptab)
detail:
| statistic | est | lower | upper |
|---|---|---|---|
| ap | 0.270 | 0.2384 | 0.302 |
| tp | 0.349 | 0.3152 | 0.384 |
| se | 0.560 | 0.4980 | 0.620 |
| sp | 0.886 | 0.8548 | 0.913 |
| diag.ac | 0.772 | 0.7408 | 0.801 |
| diag.or | 9.880 | 6.8489 | 14.251 |
| nndx | 2.244 | 1.8777 | 2.834 |
| youden | 0.446 | 0.3529 | 0.533 |
| pv.pos | 0.725 | 0.6584 | 0.784 |
| pv.neg | 0.790 | 0.7536 | 0.823 |
| lr.pos | 4.910 | 3.7613 | 6.409 |
| lr.neg | 0.497 | 0.4326 | 0.571 |
| p.rout | 0.730 | 0.6976 | 0.762 |
| p.rin | 0.270 | 0.2384 | 0.302 |
| p.tpdn | 0.114 | 0.0875 | 0.145 |
| p.tndp | 0.440 | 0.3800 | 0.502 |
| p.dntp | 0.275 | 0.2157 | 0.342 |
| p.dptn | 0.210 | 0.1773 | 0.246 |
tab:
| Outcome + | Outcome - | Total | |
|---|---|---|---|
| Test + | 150 | 57 | 207 |
| Test - | 118 | 443 | 561 |
| Total | 268 | 500 | 768 |
method: exact
digits: 2
conf.level: 0.95
pander::pander(ptab$detail[c(5,3,4,6),])
| statistic | est | lower | upper | |
|---|---|---|---|---|
| 5 | diag.ac | 0.772 | 0.741 | 0.801 |
| 3 | se | 0.560 | 0.498 | 0.620 |
| 4 | sp | 0.886 | 0.855 | 0.913 |
| 6 | diag.or | 9.880 | 6.849 | 14.251 |
par(op)
par(xpd = TRUE)
DEdataframe[,outcome] <- factor(DEdataframe[,outcome])
IDeAmodel <- rpart(paste(outcome,"~."),DEdataframe,control=rpart.control(maxdepth=3))
pr <- predict(IDeAmodel,DEdataframe,type = "class")
ptab <- list(er="Error",detail=matrix(nrow=6,ncol=1))
if (length(unique(pr))>1)
{
plot(IDeAmodel,main="IDeA",branch=0.5,uniform = TRUE,compress = TRUE,margin=0.1)
text(IDeAmodel, use.n = TRUE,cex=0.75)
ptab <- epiR::epi.tests(table(pr==0,DEdataframe[,outcome]==0))
}
pander::pander(table(DEdataframe[,outcome],pr))
| 0 | 1 | |
|---|---|---|
| 0 | 416 | 84 |
| 1 | 98 | 170 |
pander::pander(ptab)
detail:
| statistic | est | lower | upper |
|---|---|---|---|
| ap | 0.331 | 0.298 | 0.365 |
| tp | 0.349 | 0.315 | 0.384 |
| se | 0.634 | 0.574 | 0.692 |
| sp | 0.832 | 0.796 | 0.864 |
| diag.ac | 0.763 | 0.731 | 0.793 |
| diag.or | 8.591 | 6.104 | 12.090 |
| nndx | 2.144 | 1.799 | 2.704 |
| youden | 0.466 | 0.370 | 0.556 |
| pv.pos | 0.669 | 0.608 | 0.727 |
| pv.neg | 0.809 | 0.773 | 0.842 |
| lr.pos | 3.776 | 3.045 | 4.682 |
| lr.neg | 0.440 | 0.374 | 0.517 |
| p.rout | 0.669 | 0.635 | 0.702 |
| p.rin | 0.331 | 0.298 | 0.365 |
| p.tpdn | 0.168 | 0.136 | 0.204 |
| p.tndp | 0.366 | 0.308 | 0.426 |
| p.dntp | 0.331 | 0.273 | 0.392 |
| p.dptn | 0.191 | 0.158 | 0.227 |
tab:
| Outcome + | Outcome - | Total | |
|---|---|---|---|
| Test + | 170 | 84 | 254 |
| Test - | 98 | 416 | 514 |
| Total | 268 | 500 | 768 |
method: exact
digits: 2
conf.level: 0.95
pander::pander(ptab$detail[c(5,3,4,6),])
| statistic | est | lower | upper | |
|---|---|---|---|---|
| 5 | diag.ac | 0.763 | 0.731 | 0.793 |
| 3 | se | 0.634 | 0.574 | 0.692 |
| 4 | sp | 0.832 | 0.796 | 0.864 |
| 6 | diag.or | 8.591 | 6.104 | 12.090 |
par(op)
par(xpd = TRUE)
PCAdataframe[,outcome] <- factor(PCAdataframe[,outcome])
PCAmodel <- rpart(paste(outcome,"~."),PCAdataframe,control=rpart.control(maxdepth=3))
pr <- predict(PCAmodel,PCAdataframe,type = "class")
ptab <- list(er="Error",detail=matrix(nrow=6,ncol=1))
if (length(unique(pr))>1)
{
plot(PCAmodel,main="PCA",branch=0.5,uniform = TRUE,compress = TRUE,margin=0.1)
text(PCAmodel, use.n = TRUE,cex=0.75)
ptab <- epiR::epi.tests(table(pr==0,PCAdataframe[,outcome]==0))
}
pander::pander(table(PCAdataframe[,outcome],pr))
| 0 | 1 | |
|---|---|---|
| 0 | 421 | 79 |
| 1 | 88 | 180 |
pander::pander(ptab)
detail:
| statistic | est | lower | upper |
|---|---|---|---|
| ap | 0.337 | 0.304 | 0.372 |
| tp | 0.349 | 0.315 | 0.384 |
| se | 0.672 | 0.612 | 0.728 |
| sp | 0.842 | 0.807 | 0.873 |
| diag.ac | 0.783 | 0.752 | 0.811 |
| diag.or | 10.900 | 7.679 | 15.474 |
| nndx | 1.947 | 1.665 | 2.387 |
| youden | 0.514 | 0.419 | 0.600 |
| pv.pos | 0.695 | 0.635 | 0.750 |
| pv.neg | 0.827 | 0.791 | 0.859 |
| lr.pos | 4.251 | 3.415 | 5.292 |
| lr.neg | 0.390 | 0.327 | 0.465 |
| p.rout | 0.663 | 0.628 | 0.696 |
| p.rin | 0.337 | 0.304 | 0.372 |
| p.tpdn | 0.158 | 0.127 | 0.193 |
| p.tndp | 0.328 | 0.272 | 0.388 |
| p.dntp | 0.305 | 0.250 | 0.365 |
| p.dptn | 0.173 | 0.141 | 0.209 |
tab:
| Outcome + | Outcome - | Total | |
|---|---|---|---|
| Test + | 180 | 79 | 259 |
| Test - | 88 | 421 | 509 |
| Total | 268 | 500 | 768 |
method: exact
digits: 2
conf.level: 0.95
pander::pander(ptab$detail[c(5,3,4,6),])
| statistic | est | lower | upper | |
|---|---|---|---|---|
| 5 | diag.ac | 0.783 | 0.752 | 0.811 |
| 3 | se | 0.672 | 0.612 | 0.728 |
| 4 | sp | 0.842 | 0.807 | 0.873 |
| 6 | diag.or | 10.900 | 7.679 | 15.474 |
par(op)
EFAdataframe[,outcome] <- factor(EFAdataframe[,outcome])
EFAmodel <- rpart(paste(outcome,"~."),EFAdataframe,control=rpart.control(maxdepth=3))
pr <- predict(EFAmodel,EFAdataframe,type = "class")
ptab <- list(er="Error",detail=matrix(nrow=6,ncol=1))
if (length(unique(pr))>1)
{
plot(EFAmodel,main="EFA",branch=0.5,uniform = TRUE,compress = TRUE,margin=0.1)
text(EFAmodel, use.n = TRUE,cex=0.75)
ptab <- epiR::epi.tests(table(pr==0,EFAdataframe[,outcome]==0))
}
pander::pander(table(EFAdataframe[,outcome],pr))
| 0 | 1 | |
|---|---|---|
| 0 | 476 | 24 |
| 1 | 168 | 100 |
pander::pander(ptab)
detail:
| statistic | est | lower | upper |
|---|---|---|---|
| ap | 0.161 | 0.136 | 0.1894 |
| tp | 0.349 | 0.315 | 0.3838 |
| se | 0.373 | 0.315 | 0.4341 |
| sp | 0.952 | 0.929 | 0.9690 |
| diag.ac | 0.750 | 0.718 | 0.7803 |
| diag.or | 11.806 | 7.313 | 19.0590 |
| nndx | 3.076 | 2.481 | 4.0904 |
| youden | 0.325 | 0.244 | 0.4031 |
| pv.pos | 0.806 | 0.726 | 0.8719 |
| pv.neg | 0.739 | 0.703 | 0.7727 |
| lr.pos | 7.774 | 5.107 | 11.8320 |
| lr.neg | 0.658 | 0.599 | 0.7237 |
| p.rout | 0.839 | 0.811 | 0.8639 |
| p.rin | 0.161 | 0.136 | 0.1894 |
| p.tpdn | 0.048 | 0.031 | 0.0706 |
| p.tndp | 0.627 | 0.566 | 0.6849 |
| p.dntp | 0.194 | 0.128 | 0.2742 |
| p.dptn | 0.261 | 0.227 | 0.2966 |
tab:
| Outcome + | Outcome - | Total | |
|---|---|---|---|
| Test + | 100 | 24 | 124 |
| Test - | 168 | 476 | 644 |
| Total | 268 | 500 | 768 |
method: exact
digits: 2
conf.level: 0.95
pander::pander(ptab$detail[c(5,3,4,6),])
| statistic | est | lower | upper | |
|---|---|---|---|---|
| 5 | diag.ac | 0.750 | 0.718 | 0.780 |
| 3 | se | 0.373 | 0.315 | 0.434 |
| 4 | sp | 0.952 | 0.929 | 0.969 |
| 6 | diag.or | 11.806 | 7.313 | 19.059 |
par(op)